SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 391–400 of 800 papers

TitleStatusHype
Intrusion Detection using Continuous Time Bayesian Networks—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Intrusion Detection using Sequential Hybrid Model—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization—0
IoT Behavioral Monitoring via Network Traffic Analysis—0
IoT Botnet Detection Using an Economic Deep Learning Model—0
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments—0
IT Intrusion Detection Using Statistical Learning and Testbed Measurements—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified